Method for processing behavior data, method for controlling autonomous vehicle, and autonomous vehicle

ABSTRACT

A method for processing behavior data, a method for controlling an autonomous vehicle, apparatuses thereof, a device, a storage medium, a computer program product, and an autonomous vehicle are provided. The method includes: acquiring historical driving data, the historical driving data comprising lane-level navigation data; and performing data mining on the historical driving data to obtain driving feature information, the driving feature information comprising at least one of: a lane-change position feature, a traveling speed feature, or a traveling path feature.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims the priority of Chinese PatentApplication No. 202210556131.2, titled “METHOD FOR PROCESSING BEHAVIORDATA, METHOD FOR CONTROLLING AUTONOMOUS VEHICLE, AND AUTONOMOUSVEHICLE”, filed on May 19, 2022, the content of which is incorporatedherein by reference in its entirety.

TECHNICAL FIELD

The present disclosure relates to the technical field of artificialintelligence, in particular to the technical fields of autonomousdriving, big data and deep learning, and more particularly, to a methodfor processing behavior data, a method for controlling an autonomousvehicle and apparatuses thereof, a device, a storage medium, a computerprogram product, and an autonomous vehicle.

BACKGROUND

With the gradual popularization of artificial intelligence technologyand the fifth-generation mobile communication technology, autonomousdriving technology has developed rapidly, and the use of autonomousdriving technology in vehicles is also increasing. Usually, anautonomous driving function is implemented mainly relying on basictechnologies such as machine vision, radar positioning, satellitepositioning and intelligent control. How to ensure and continuouslyimprove the safety of autonomous driving has always been one of keyissues in the field of autonomous driving.

SUMMARY

The present disclosure provides a method for processing behavior data, amethod for controlling an autonomous vehicle, apparatuses thereof, adevice, a storage medium, a computer program product, and an autonomousvehicle, which improve the safety of autonomous driving.

Some embodiments of the present disclosure provide a method forprocessing behavior data, including: acquiring historical driving data,the historical driving data comprising lane-level navigation data; andperforming data mining on the historical driving data to obtain drivingfeature information, the driving feature information comprising at leastone of: a lane-change position feature, a traveling speed feature, or atraveling path feature.

Some embodiments of the present disclosure provide a method forcontrolling an autonomous vehicle, including: acquiring driving featureinformation, wherein the driving feature information is obtained by themethod according to the above method for processing behavior data;acquiring perceptual positioning information at a vehicle end;controlling the autonomous vehicle, based on the driving decision-makinginformation.

Some embodiments of the present disclosure provide an apparatus forprocessing behavior data, including: an acquisition module, configuredto acquire historical driving data, the historical driving datacomprising lane-level navigation data; and a mining module, configuredto perform data mining on the historical driving data to obtain drivingfeature information, the driving feature information comprising at leastone of: a lane-change position feature, a traveling speed feature, or atraveling path feature.

Some embodiments of the present disclosure provide an apparatus forcontrolling an autonomous vehicle, including: a first acquisitionmodule, configured to acquire driving feature information, wherein thedriving feature information is obtained by the apparatus for processingbehavior data; a second acquisition module, configured to acquireperceptual positioning information at a vehicle end; a decision-makingmodule, configured to generate driving decision-making information basedon the driving feature information and the perceptual positioninginformation; and a controlling module, configured to control theautonomous vehicle, based on the driving decision-making information.

Some embodiments of the present disclosure provide an electronic device,including: at least one processor; and a memory communicativelyconnected to the at least one processor; where the memory storesinstructions executable by the at least one processor, and theinstructions are executed by the at least one processor, such that theat least one processor can execute the above method for processingbehavior data or the method for controlling an autonomous vehicle.

Some embodiments of the present disclosure provide a computer readablestorage medium storing computer instructions, where the computerinstructions are used for causing a computer to execute the above methodfor processing behavior data or the method for controlling an autonomousvehicle.

Some embodiments of the present disclosure provide a computer programproduct, including a computer program, where the computer program, whenexecuted by a processor, implements the above method for processingbehavior data or the method for controlling an autonomous vehicle.

Some embodiments of the present disclosure provide an autonomousvehicle, including at least one processor; and a memory communicativelyconnected to the at least one processor; where the memory storesinstructions executable by the at least one processor, and theinstructions are executed by the at least one processor, such that theat least one processor can execute the above method for processingbehavior data or the method for controlling an autonomous vehicle.

It should be understood that contents described in the SUMMARY areneither intended to identify key or important features of embodiments ofthe present disclosure, nor intended to limit the scope of the presentdisclosure. Other features of the present disclosure will become readilyunderstood with reference to the following description.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are used for better understanding of thepresent solution, and do not constitute a limitation to the presentdisclosure. In which:

FIG. 1 is an exemplary system architecture diagram to which the presentdisclosure may be applied;

FIG. 2 is a flowchart of an embodiment of a method for processingbehavior data according to the present disclosure;

FIG. 3 is a flowchart of another embodiment of the method for processingbehavior data according to the present disclosure;

FIG. 4 is a flowchart of yet another embodiment of the method forprocessing behavior data according to the present disclosure;

FIG. 5 is a flowchart of an embodiment of a method for controlling anautonomous vehicle according to the present disclosure;

FIG. 6 is a schematic diagram of a process of generating drivingdecision-making information according to the present disclosure;

FIG. 7 is a schematic structural diagram of an embodiment of anapparatus for processing behavior data according to the presentdisclosure;

FIG. 8 is a schematic structural diagram of an embodiment of anapparatus for controlling an autonomous vehicle according to the presentdisclosure; and

FIG. 9 is a block diagram of an electronic device used to implement themethod for processing behavior data or the method for controlling anautonomous vehicle according to embodiments of the present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS

Exemplary embodiments of the present disclosure are described below withreference to the accompanying drawings, where various details of theembodiments of the present disclosure are included to facilitateunderstanding, and should be considered merely as exemplary. Therefore,those of ordinary skills in the art should realize that various changesand modifications can be made to the embodiments described hereinwithout departing from the scope and spirit of the present disclosure.Similarly, for clearness and conciseness, descriptions of well-knownfunctions and structures are omitted in the following description.

FIG. 1 shows an exemplary system architecture 100 to which embodimentsof a method for processing behavior data or a method for controlling anautonomous vehicle of the present disclosure may be applied.

As shown in FIG. 1 , the system architecture 100 may include anautonomous vehicle 101, a general vehicle 102, a terminal device 103, anetwork 104 and a server 105. The network 104 serves as a medium forproviding a communication link between the autonomous vehicle 101, thegeneral vehicle 102, the terminal device 103 and the server 105. Thenetwork 103 may include various types of connections, such as wired orwireless communication links, or optical cables.

The autonomous vehicle 101, the general vehicle 102, and the terminaldevice 103 may interact with the server 105 through the network 104.Various intelligent vehicle-end applications, such as intelligentdriving, intelligent navigation applications, may be installed on theautonomous vehicle 101; a driving assistance system, a navigationapplication module, etc. may be installed on the general vehicle; anavigation application, a map application, etc. may be installed on theterminal device 103. The server 105 may provide various behavior dataprocessing services. For example, the server 105 may acquire drivingpath information during driving or navigation from the autonomousdriving vehicle 101, the general vehicle 102 and the terminal device103, and acquire captured image data from in-vehicle cameras of theautonomous vehicle 101 and the general vehicle 102, then mine these datato obtain driving feature information, and send the driving featureinformation to the autonomous vehicle 101. The autonomous vehicle 101may generate driving decision-making information based on the receiveddriving feature information, and perform autonomous driving behaviorsbased on the decision-making information.

It should be noted that the terminal device 103 may be hardware orsoftware. When the terminal device 103 is hardware, it may be variouselectronic devices, including but not limited to smart phones, tabletcomputers, laptop computers, desktop computers, or the like. When theterminal device 103 is software, it may be installed in the aboveelectronic devices. The terminal device 103 may be implemented as aplurality of software or software modules, or may be implemented as asingle software or software module, which is no limited herein. Theserver 105 may be hardware or software. When the server 105 is hardware,it may be implemented as a distributed server cluster composed of aplurality of servers, or may be implemented as a single server. When theserver 105 is software, it may be implemented as a plurality of softwareor software modules (for example, for providing distributed services),or may be implemented as a single software or software module, which isnot limited herein.

It should be understood that the numbers of autonomous vehicles, generalvehicles, terminal devices, networks, and servers in FIG. 1 are merelyillustrative. Any number of autonomous vehicles, general vehicles,terminal devices, networks, and servers may be provided according toimplementation needs.

It should also be noted that the method for processing behavior dataprovided by embodiments of the present disclosure is generally performedby the server 105, and accordingly, an apparatus for processing behaviordata is generally provided in the server 105. In addition, in theembodiments of the present disclosure, behavior data to be processed bythe method for processing behavior data may be behavior data generatedby vehicles during past driving, that is, historical driving data.

With further reference to FIG. 2 , illustrating a flow 200 of anembodiment of a method for processing behavior data according to thepresent disclosure. The method includes the following step 201 to step202.

Step 201, acquiring historical driving data, the historical driving dataincluding lane-level navigation data.

In the present embodiment, an executing body of the method forprocessing behavior data (for example, the server 105 shown in FIG. 1 )may first acquire the historical driving data. Here, the historicaldriving data refers to driving data generated by various types ofvehicles during past driving. This data mainly includes the lane-levelnavigation data, and may also include high-accuracy map data. Inaddition to content of general maps, high-accuracy maps also store a lotof driving assistance information as structured data, which may includeroad data, such as lane information, e.g., a position, a type, a width,a slope and a curvature of a lane line, and may also include informationabout fixed objects around lanes, for example, information such astraffic signs, or traffic lights, road details such as lane heightlimits, sewer outlets, or obstacles, and infrastructure information suchas overhead objects, guardrails, trees, road edge types, or roadsidelandmarks. The lane-level navigation data includes navigation trajectoryinformation generated based on the high-accuracy maps.

In the present embodiment, the historical driving data are not limitedto be derived from the autonomous vehicles, but may also be derived fromthe general vehicles or navigation applications in terminal devices. Forexample, after completing each navigation event or driving event, theautonomous vehicle may send generated navigation data to the server asthe historical driving data. After completing each navigation task, anavigation module or navigation application integrated in the generalvehicle, as well as a navigation application in the terminal device mayalso send generated navigation data to the server as the historicaldriving data.

Step 202, performing data mining on the historical driving data toobtain driving feature information, the driving feature informationincluding at least one of: a lane-change position feature, a travelingspeed feature, or a traveling path feature.

In the present embodiment, after obtaining the historical driving data,the executing body of the method for processing behavior data needs toperform further data mining on the historical driving data to obtain therequired driving feature information. Specifically, due to a relativelylarge volume of the acquired historical driving data, big data analysisand mining methods, such as classification, regression analysis,clustering, association rules, feature analysis, or variation anddeviation analysis, may be used to perform mining on the historicaldriving data from different perspectives, to obtain one or more of thelane-change position feature, the travelling speed feature, and thetraveling path feature, as a component of the driving featureinformation. The lane-change position feature may be used to represent alane-change position selected by a vehicle when changing a lane, thetraveling speed feature may be used to represent a speed of a vehicleduring driving, and the traveling path feature may be used to representoptional paths between two geographic positions.

In some alternative implementations of the present embodiment, thehistorical driving data further includes in-vehicle image data; and thedriving feature information includes at least one of: a dangerousscenario feature, a dynamic event feature, and a road-surface conditionfeature.

Specifically, the in-vehicle image data may be image data captured,while the vehicle is traveling, by an in-vehicle camera installed on theautonomous vehicle or the general vehicle. By performing mining on thehistorical driving data including the in-vehicle image data, one or moreof the dangerous scenario feature, the dynamic event feature, and theroad-surface condition feature may be further obtained, each also usedas a component of the driving feature information. The dangerousscenario feature may be used to represent a scenario in which, forexample, accidents are more likely to occur during driving, such as awinding mountain-road scenario. The dynamic event feature may be used torepresent a temporary event that affects a driving process, such as atraffic accident. The road-surface condition feature may be used torepresent road-surface information such as traffic road-surfacematerials that affect perception of the autonomous vehicles, such as awet and slippery road surface. By performing mining on the in-vehicleimage data, comprehensiveness of the driving feature information may befurther improved.

In the method for processing behavior data provided by the aboveembodiment of the present disclosure, first the historical driving datais acquired, the historical driving data including the lane-levelnavigation data, and then data mining is performed on the historicaldriving data to obtain the driving feature information, the drivingfeature information including at least one of: the lane-change positionfeature, the traveling speed feature, and the traveling path feature. Byfully performing mining on the historical driving data, accurate andcomprehensive driving feature information may be obtained.

With further reference to FIG. 3 , illustrating a flow 300 of anotherembodiment of the method for processing behavior data according to thepresent disclosure. The method includes the following step 301 to step308.

Step 301, acquiring historical driving data, the historical driving dataincluding lane-level navigation data.

In the present embodiment, the specific operation of step 301 has beendescribed in detail in step 201 in the embodiment shown in FIG. 2 , anddetailed description thereof will be omitted.

In the present embodiment, the obtained driving feature information mayinclude one or more of: the lane-change position feature, the travellingspeed feature, and the traveling path feature. If the following steps302-304 are performed, the obtained driving feature information mayinclude the lane-change position feature. If the following steps 305-306are performed, the obtained driving feature information may include thetraveling speed feature. If the following steps 307-308 are performed,the obtained driving feature information may include the traveling pathfeature. The method for processing behavior data in the presentembodiment may include all of steps 302-308, or may include one or moreof steps 302-304, steps 305-306, and steps 307-308.

Step 302, acquiring lane-change data in multiple lane-change scenariosfrom the lane-level navigation data, where the lane-change scenariosinclude an intersection lane-change scenario.

In the present embodiment, after obtaining the lane-level navigationdata, the executing body of the method for processing behavior data mayfirst screen out the lane-change data in the multiple lane-changescenarios from the navigation data. The lane-change scenarios mayinclude the intersection lane-change scenario or a through lane-changescenario. Intersections may include crossroads, T-junctions, entrancesand exits, U-turn intersections, etc., so as to cover lane-changesituations of vehicles in scenarios such as intersections, entrances andexits, or expressway ramps. The through lane-change scenario includes asituation where a vehicle changes a lane in a through lane. Thelane-change data may include driving trajectory information of thevehicle within a predetermined time period or a predetermined distancebefore and after changing the lane.

Step 303, classifying lane-change data in each lane-change scenario toobtain multiple sets of lane-change data according to a traffic flowlevel.

In the present embodiment, after obtaining the lane-change data in themultiple lane-change scenarios, the executing body of the method forprocessing behavior data may process the lane-change data in eachlane-change scenario respectively. Specifically, the lane-change data ina lane change scenario may be classified according to the traffic flowlevel. The traffic flow level may include three levels of a high flow, amedium flow and a low flow. Specifically, a PV value of a daily averagetraffic volume of the road where a vehicle is located may be used as aclassifying basis. For example, lane-change data generated on a roadwith a PV value greater than 200 may be classified into a high flow set,lane change data generated on a road with a PV value less than 40 may beclassified into a low flow set, and the remaining lane-change data maybe classified into a medium flow set. In this way, the multiple sets oflane-change data corresponding to multiple flow levels may be obtained.

Step 304, performing lane-change position clustering on each set oflane-change data respectively to obtain multiple lane-change positionfeatures.

In the present embodiment, each lane-change position feature correspondsto a traffic flow level in a lane-change scenario. Specifically, afterobtaining the multiple sets of lane-change data, the executing body ofthe method for processing behavior data may perform clustering operationon each set of data respectively, and may use a commonly used clusteringalgorithm (such as K-means or hierarchical clustering algorithm) toperform the clustering operation, and then use a clustering result asthe lane-change position feature, so as to obtain the multiplelane-change position features. Since each set of lane-change datacorresponds to a traffic flow level in a lane-change scenario, theobtained lane-change position features may also be distinguishedaccording to the lane-change scenario and the traffic flow level.

By classifying the lane-level navigation data according to thelane-change scenario and the traffic flow level, the finally obtainedlane-change position features are more targeted and suitable for avariety of lane-change situations.

Step 305, acquiring traveling speed data at different geographicpositions from the lane-level navigation data.

In the present embodiment, after obtaining the lane-level navigationdata, the executing body of the method for processing behavior data mayacquire traveling speed data of each vehicle at the geographic positionsbased on the driving trajectories and traveling speeds of the vehiclesin the lane-level navigation data, so that multiple traveling speed datamay be obtained at one geographical position. The geographic positionmay be a specific coordinate point in a high-accuracy map.

Step 306, clustering traveling speed data at each geographic positionrespectively to obtain an average speed value, an average accelerationvalue and an extreme speed value at the geographic position, as thetraveling speed feature.

In the present embodiment, the executing body of the method forprocessing behavior data may use a clustering algorithm to cluster thetraveling speed data at each geographic position respectively. Inparticular, a density clustering algorithm in machine learning, such asDBSCAN (Density-Based Spatial Clustering of Applications with Noise),may be used for clustering. The obtained clustering result may includethe average speed value, the average acceleration value and the extremespeed value at each geographic position. The average speed value may bean average speed of multiple vehicles traveling at the geographicposition, the average acceleration value may be an average accelerationused when multiple vehicles accelerate or decelerate at the geographicposition, and the extreme speed value may be the maximum and minimumspeeds of multiple vehicles traveling at the geographic position. Theaverage speed value, the average acceleration value and the extremespeed value at each geographic position may be used collectively as thetraveling speed feature.

By clustering the traveling speed data at the different geographicpositions respectively, the obtained traveling speed feature mayaccurately correspond to coordinates of the geographic positions, whichimproves reliability of the traveling speed feature.

Step 307, acquiring traveling path data from the lane-level navigationdata.

In the present embodiment, the executing body of the method forprocessing behavior data may determine a vehicle a traveling trajectoryfor each navigation process from the lane-level navigation data, anddetermine a path traveled by the vehicle from a starting position to anend position of this navigation according to the traveling trajectory.The traveling path between the two geographic positions obtained in thisway may be used as the acquired traveling path data. It may beunderstood that the traveling path may be affected by factors such asroad traffic conditions during navigation, or preferences of anavigation user. Therefore, the traveling path between two specificgeographic positions may include various road combinations.

Step 308, classifying the traveling path data to obtain a recommendedpath feature and a dangerous path feature as the traveling path feature.

In the present embodiment, after acquiring the traveling path data, theexecuting body of the method for processing behavior data may use astatistical analysis method in statistics to classify the traveling pathdata, or input the traveling path data into a pre-trained classificationmodel for classification. After classifying the traveling path data, therecommended path feature and the dangerous path feature may be obtained.The recommended path feature may be used to represent a road combinationthat is travelled more frequently between two geographic positions, andis often used as a planning path for driving decision-making; thedangerous path feature may be used to represent a road combination thatis travelled less frequently between two geographic positions, and wheredangerous driving behaviors (for example, travelling on the wrong sideof the road, illegal lane change, etc.) often occur. After theclassification, for those traveling path features that cannot beclassified into the recommended path feature or the dangerous pathfeature, other path features may also be formed, which may also be usedas part of the traveling path feature.

By classifying the traveling path data, the obtained traveling pathfeature may include the recommended path feature and the dangerous pathfeature, which improves reliability and safety of the traveling pathfeature.

With further reference to FIG. 4 , illustrating a flow 400 of yetanother embodiment of the method for processing behavior data accordingto the present disclosure. The method includes the following step 401 tostep 408.

Step 401, acquiring historical driving data, the historical driving dataincluding in-vehicle image data.

In the present embodiment, the specific operation of step 401 has beendescribed in detail in step 201 in the embodiment shown in FIG. 2 , anddetailed description thereof will be omitted.

In the present embodiment, the historical driving data may include thein-vehicle image data, and may also include both the lane-levelnavigation data and the high-accuracy maps.

In the present embodiment, the obtained driving feature information mayinclude one or more of: a dangerous scenario feature, a dynamic eventfeature, and a road-surface condition feature. If the following steps402-404 are performed, the obtained driving feature information mayinclude the dangerous scenario feature. If the following steps 405-406are performed, the obtained driving feature information may include thedynamic event feature. If the following steps 407-408 are performed, theobtained driving feature information may include the road-surfacecondition feature. The method for processing behavior data in thepresent embodiment may include all of steps 402-408, or may include oneor more of steps 402-404, steps 405-406, and steps 407-408. It should benoted that steps 402-408 in the present embodiment do not conflict withsteps 302-308 in FIG. 3 , and they may exist at the same time. In somealternative implementations of the present embodiment, the method forprocessing behavior data may include one or more of steps 302-304, steps305-306, steps 307-308, steps 402-404, steps 405-406 and steps 407-408.

Step 402, extracting driving scenario data from the in-vehicle imagedata.

In the present embodiment, the executing body of the method forprocessing behavior data may first convert video data in the in-vehicleimage data into image data. For example, image frames representative ofscenarios in the video data may be retained, and the remaining framesmay be deleted. Similar filtering may also be performed on the imagedata in the in-vehicle image, and the finally obtained driving scenariodata includes multiple images with driving scenarios as the main displaycontent.

Step 403, classifying the driving scenario data to obtain multipledangerous scenarios.

In the present embodiment, the executing body of the method forprocessing behavior data may use an image classification method toclassify the multiple images included in the driving scenario data toobtain the multiple dangerous scenarios. A dangerous scenario refers toa scenario in which incidents such as accidents are relatively prone tooccur during driving, and may specifically include a curve scenario, anda laneway scenario. The curve scenario may include a curve scenario in acity road, also may include a curve scenario in a mountain road or inthe highway. The laneway scenario mainly corresponds to a scenario wherethe road is extremely narrow and there are many obstacles on both sides.It may be understood that the above examples of specific dangerousscenarios do not constitute a limitation on the present embodiment.Scenarios that may affect driving safety, such as potholes and unpavedroads, may all be used as dangerous scenarios in the present embodiment.

Step 404, using an image feature in each dangerous scenario as thedangerous scenario feature.

In the present embodiment, the image classification method in the abovestep 403 is based on image features for classification, so, after theclassification, the image feature corresponding to each dangerousscenario may be obtained, and these image features may be used as thedangerous scenario feature.

In some alternative implementations of the present embodiment, theexecuting body of the method for processing behavior data may directlyinput the obtained in-vehicle image data into an image classificationmodel based on deep learning. Since the model mainly focuses on theimage features related to the dangerous scenarios during previoustraining, it may directly used to classify the in-vehicle image data andoutput the dangerous scenario feature.

By classifying and identifying the driving scenario data in thein-vehicle image data, accurate dangerous scenario feature may beobtained, which improves the comprehensiveness of the driving featureinformation.

Step 405, identifying a dynamic event in the in-vehicle image data.

In the present embodiment, the dynamic event includes at least one of: aconstruction event, or an accident event. Specifically, the dynamicevent may refer to a non-fixed event that affects vehicle traveling,such as some temporary events that occur on the road, includingconstruction events, accident events, traffic control events, or thelike. When identifying the dynamic event, the executing body of themethod for processing behavior data in the present embodiment mayidentify information such as accidents, construction signs in imagesthrough a deep learning algorithm to obtain the dynamic event by mining.Specifically, the in-vehicle image data may be input into a deeplearning convolutional neural network algorithm model for dynamic eventidentification.

Step 406, using an identification result as the dynamic event feature.

In the present embodiment, since the model used in step 405 mainlyfocuses on image features related to the dynamic event during previoustraining, the output identification result may be directly used as thedynamic event feature.

By identifying the dynamic event in the in-vehicle image data, theobtained dynamic event feature may be used as part of the drivingfeature information, which further improves the comprehensiveness of thedriving feature information.

Step 407, extracting road-surface image data from the in-vehicle imagedata.

In the present embodiment, the executing body of the method forprocessing behavior data may first extract the road-surface image datarelated to road-surface conditions from the in-vehicle image data. Forexample, an image in which road surface is located may be interceptedfrom videos or images captured by an in-vehicle camera, as theroad-surface image data.

Step 408, identifying the road-surface image data to obtain theroad-surface condition feature.

In the present embodiment, the road-surface condition feature includesat least one of: a wear feature of a road surface, a slippery feature ofa road surface, a pothole feature of a road surface, or an obstaclefeature of a road surface. The executing body of the method forprocessing behavior data may use a deep learning algorithm to processthe road-surface image data to obtain the road-surface conditionfeature.

In some alternative implementations of the present embodiment, theexecuting body of the method for processing behavior data may directlyinput the obtained in-vehicle image data into an image classificationmodel based on deep learning. Since the model mainly focuses on imagefeatures related to road-surface conditions during previous training, itmay directly identify the in-vehicle image data and output theroad-surface condition feature.

By identifying the road-surface conditions in the in-vehicle image data,the obtained road-surface condition feature may be used as part of thedriving feature information, which further improves thecomprehensiveness of the driving feature information.

With further reference to FIG. 5 , illustrating a flow 500 of anembodiment of a method for controlling an autonomous vehicle accordingto the present disclosure, and the method for controlling includes thefollowing step 501 to step 504.

Step 501, acquiring driving feature information.

In the present embodiment, an executing body of the method forcontrolling an autonomous vehicle (for example, the autonomous vehicle101 shown in FIG. 1 ) may acquire the driving feature information from aserver. The driving feature information may be obtained by the serveraccording to the method for processing behavior data of any one of theembodiments in FIG. 2 to FIG. 4 . The driving feature information mayinclude one or more of the lane-change position feature, the travelingspeed feature, the traveling path feature, the dangerous scenariofeature, the dynamic event feature, and the road-surface conditionfeature.

Step 502, acquiring perceptual positioning information at a vehicle end.

In the present embodiment, the executing body of the method forcontrolling an autonomous vehicle may also acquire the perceptualinformation at the vehicle end through a sensor provided at the vehicleend. The sensor at the vehicle end may be a point cloud sensor or animage sensor. The point cloud sensor is a sensor that may collect pointcloud data, and is generally a 3D (3-dimension) sensor. The point cloudsensor includes a light detection and ranging (Lidar) sensor and a radiodetection and ranging (Radar) sensor. The image sensor is a sensor thatmay collect images, and is generally a 2D (2-dimension) sensor, such asa camera sensor. After obtaining the images and the point cloud data,the perceptual positioning information may be acquired through aperceptual positioning algorithm model. The perceptual positioninginformation may include static traffic elements such as road-surfacemarkings or obstacles, and dynamic elements such as vehicles orpedestrians.

Step 503, generating driving decision-making information based on thedriving feature information and the perceptual positioning information.

In the present embodiment, the executing body of the method forcontrolling an autonomous vehicle may perform a multi-modal informationfusion operation on the driving feature information and the perceptualpositioning information, and generate the driving decision-makinginformation based on fused information. The driving decision-makinginformation may be used to instruct the autonomous vehicle to generate avehicle driving task, perform travelling path planning, or performexception handling, or the like.

In some alternative implementations, based on pre-established drivingdecision-making rules, the driving feature information and theperceptual positioning information may be fused to generate the drivingdecision-making information. In some other alternative implementations,the driving feature information and the perceptual positioninginformation may also be input into a deep learning multimodalinformation fusion model, and the driving decision-making informationmay be inferred through the model.

Step 504, controlling the autonomous vehicle, based on the drivingdecision-making information.

In the present embodiment, the executing body of the method forcontrolling an autonomous vehicle may control the autonomous vehicle toperform an autonomous driving task, perform path planning, and handle anexceptional event, or the like, based on the generated drivingdecision-making information.

In some alternative implementations of the present embodiment, themethod 600 for controlling an autonomous vehicle further includes:acquiring V2X information for vehicle-use wireless communication; andthe generating driving decision-making information based on the drivingfeature information and the perceptual positioning information,includes: generating the driving decision-making information based onthe driving feature information, the perceptual positioning informationand the V2X information.

In the present embodiment, the executing body of the method forcontrolling an autonomous vehicle may also acquire the V2X information.V2X (vehicle to X or Vehicle to Everything) refers to vehicle-usewireless communication technology, also called vehicle-to-everythingcommunication, which enables vehicles to obtain a series of trafficinformation such as real-time road conditions, road information, andpedestrian information, improving driving safety, reducing congestion,improving a traffic efficiency, etc. Here, V represents the vehicle, andX represents any object that interacts with the vehicle. Currently, Xmainly includes a vehicle (Vehicle to Vehicle, V2V), a person (Vehicleto Pedestrian, V2P), a traffic roadside infrastructure (Vehicle toInfrastructure, V2I) and a Network (Vehicle to Network, V2N). The V2Xinformation may include surrounding environment information such assurrounding infrastructures, other vehicles, or pedestrians.

When generating the driving decision-making information, multi-modalinformation fusion operation may be performed on the driving featureinformation, the perceptual positioning information and the V2Xinformation to generate the driving decision-making information, and thedriving decision-making information is generated based on the fusedinformation. For the method for generating, reference may be made to theabove step 603, and detailed description thereof will be omitted.

As can be seen from FIG. 5 , the method for controlling an autonomousvehicle in the present embodiment first acquires the driving featureinformation and the perceptual positioning information at the vehicleend, then generates the driving decision-making information based on thedriving feature information and the perceptual positioning information,and finally controls the autonomous vehicle based on the drivingdecision-making information. By generating the driving decision-makinginformation based on the driving feature information, the drivingdecision-making information may be obtained on the basis of referring tobig data of driving history, which improves the safety of autonomousdriving.

For ease of understanding, FIG. 6 shows a schematic diagram of a processof generating driving decision-making information in an embodiment ofthe present disclosure. As shown in FIG. 6 , a server obtains drivingfeatures by mining historical driving data, which may specificallyinclude a lane-change position feature, a traveling speed feature, atraveling path feature, a dangerous scenario feature, a dynamic eventfeature and a road-surface condition feature. An autonomous vehicle mayacquire all the driving features mined from the server, and acquirevehicle-end perceptual information through a vehicle-end sensor, mayalso acquire V2X information through roadside equipment, and thenperform multi-modal information fusion on the driving features, thevehicle-end perceptual information, and the V2X information to obtaindriving decision-making information including task decision-making,trajectory planning, and exception handling.

With further reference to FIG. 7 , as an implementation of the methodshown in FIG. 2 -FIG. 4 , the present disclosure provides an embodimentof an apparatus for processing behavior data, and the apparatusembodiment corresponds to the method embodiments as shown in FIG. 2-FIG. 4 . The apparatus may be applied to various servers.

As shown in FIG. 7 , the apparatus 700 for processing behavior data inthe present embodiment may include an acquisition module 701 and amining module 702. The acquisition module 701 is configured to acquirehistorical driving data, the historical driving data includinglane-level navigation data. The mining module 702 is configured toperform data mining on the historical driving data to obtain drivingfeature information, the driving feature information including at leastone of: a lane-change position feature, a traveling speed feature, or atraveling path feature.

In the present embodiment, in the apparatus 700 for processing behaviordata: for the specific processing and the technical effects of theacquisition module 701 and the mining module 702, reference may be madeto the relevant descriptions of steps 201-202 in the correspondingembodiment of FIG. 2 , and detailed description thereof will be omitted.

In some alternative implementations of the present embodiment, thehistorical driving data further includes in-vehicle image data; and thedriving feature information includes at least one of: a dangerousscenario feature, a dynamic event feature, and a road-surface conditionfeature.

In some alternative implementations of the present embodiment, thedriving feature information includes the lane-change position feature,and the mining module 702 includes: a first acquisition unit, configuredto acquire lane-change data in multiple lane-change scenarios from thelane-level navigation data, where the lane-change scenarios include anintersection lane-change scenario; a flow classification unit,configured to perform classifying, for lane-change data in eachlane-change scenario, to obtain multiple sets of lane-change dataaccording to a traffic flow level; and a position clustering unit,configured to perform lane-change position clustering on each set oflane-change data respectively to obtain multiple lane-change positionfeatures, where each lane-change position feature corresponds to a classof traffic flow level in a lane-change scenario.

In some alternative implementations of the present embodiment, thedriving feature information includes the traveling speed feature, andthe mining module 702 includes: a second acquisition unit, configured toacquire traveling speed data at different geographic positions from thelane-level navigation data; and a speed clustering unit, configured tocluster traveling speed data at each geographic position respectively toobtain an average speed value, an average acceleration value and anextremum speed value at the different geographic positions, as thetraveling speed feature.

In some alternative implementations of the present embodiment, thedriving feature information includes the traveling path feature, and themining module 702 includes: a third acquisition unit, configured toacquire traveling path data from the lane-level navigation data; and apath classifying unit, configured to classify the traveling path data toobtain a recommended path feature and a dangerous path feature as thetraveling path feature.

In some alternative implementations of the present embodiment, thedriving feature information includes the dangerous scenario feature, andthe mining module 702 includes: a fourth acquisition unit, configured toextract driving scenario data from the in-vehicle image data; a scenarioclassifying unit, configured to classify the driving scenario data toobtain multiple dangerous scenarios, where the multiple dangerousscenarios include a curve-road scenario, a laneway scenario; and afeature determining unit, configured to use image features in eachdangerous scenario as the dangerous scenario feature.

In some alternative implementations of the present embodiment, thedriving feature information includes the dynamic event feature, and themining module 702 includes: an event identifying unit, configured toidentify a dynamic event in the in-vehicle image data, where the dynamicevent includes at least one of: a construction event, or an accidentevent; and an event determining unit, configured to use anidentification result as the dynamic event feature.

In some alternative implementations of the present embodiment, thedriving feature information includes the road-surface condition feature,and the mining module 702 includes: a fifth acquisition unit, configuredto extract road-surface image data from the in-vehicle image data; and aroad surface identifying unit, configured to identify the road-surfaceimage data to obtain the road-surface condition feature, where theroad-surface condition feature includes at least one of: a wear featureof a road surface, a slippery feature of a road surface, a potholefeature of a road surface, or an obstacle feature of a road surface.

With further reference to FIG. 8 , as an implementation of the methodshown in FIG. 5 , the present disclosure provides an embodiment of anapparatus for controlling an autonomous vehicle, and the apparatusembodiment corresponds to the method embodiment as shown in FIG. 5 . Theapparatus may be applied to autonomous vehicles.

As shown in FIG. 8 , the apparatus 800 for controlling an autonomousvehicle in the present embodiment may include a first acquisition module801, a second acquisition module 802, a decision-making module 803 and acontrolling module 804. The first acquisition module 801 is configuredto acquire driving feature information, where the driving featureinformation is obtained by the apparatus for processing behavior datashown in FIG. 7 . The second acquisition module 802 is configured toacquire perceptual positioning information at a vehicle end. Thedecision-making module 803 is configured to generate drivingdecision-making information based on the driving feature information andthe perceptual positioning information. The controlling module 804 isconfigured to control the autonomous vehicle, based on the drivingdecision-making information.

In the present embodiment, in the apparatus 800 for controlling anautonomous vehicle: for the specific processing and the technicaleffects of the first acquisition module 801, the second acquisitionmodule 802, the decision-making module 803 and the controlling module804, reference may be made to the relevant descriptions of steps 501-502in the corresponding embodiment of FIG. 5 , and detailed descriptionthereof will be omitted.

The apparatus 800 for controlling further includes: a third acquisitionmodule, configured to acquire V2X information for vehicle wirelesscommunication; and the decision-making module 803 includes: adecision-making unit, configured to generate the driving decision-makinginformation based on the driving feature information, the perceptualpositioning information and the V2X information.

According to an embodiment of the present disclosure, the presentdisclosure also provides an electronic device, a readable storagemedium, a computer program product and an autonomous vehicle.

FIG. 9 shows a schematic block diagram of an example electronic device900 that may be used to implement embodiments of the present disclosure.The electronic device is intended to represent various forms of digitalcomputers, such as laptop computers, desktop computers, workbenches,personal digital assistants, servers, blade servers, mainframecomputers, and other suitable computers. The electronic device may alsorepresent various forms of mobile apparatuses, such as personal digitalprocessors, cellular phones, smart phones, wearable devices, and othersimilar computing apparatuses. The parts shown herein, their connectionsand relationships, and their functions are merely examples, and are notintended to limit the implementation of the present disclosure describedand/or claimed herein.

As shown in FIG. 9 , the device 900 includes a computation unit 901,which may perform various appropriate actions and processing, based on acomputer program stored in a read-only memory (ROM) 902 or a computerprogram loaded from a storage unit 908 into a random access memory (RAM)903. In the RAM 903, various programs and data required for theoperation of the device 900 may also be stored. The computation unit901, the ROM 902, and the RAM 903 are connected to each other through abus 904. An input/output (I/O) interface 905 is also connected to thebus 904.

A plurality of parts in the device 900 are connected to the I/Ointerface 905, including: an input unit 906, for example, a keyboard anda mouse; an output unit 907, for example, various types of displays andspeakers; the storage unit 908, for example, a disk and an optical disk;and a communication unit 909, for example, a network card, a modem, or awireless communication transceiver. The communication unit 909 allowsthe device 900 to exchange information/data with other devices over acomputer network such as the Internet and/or various telecommunicationnetworks.

The computation unit 901 may be various general-purpose and/or dedicatedprocessing components having processing and computing capabilities. Someexamples of the computation unit 901 include, but are not limited to,central processing unit (CPU), graphics processing unit (GPU), variousdedicated artificial intelligence (AI) computing chips, variouscomputation units running machine learning model algorithms, digitalsignal processors (DSP), and any appropriate processors, controllers,microcontrollers, etc. The computation unit 901 performs the variousmethods and processes described above, such as the method for processingbehavior data or the method for controlling an autonomous vehicle. Forexample, in some embodiments, the method for processing behavior data orthe method for controlling an autonomous vehicle may be implemented as acomputer software program, which is tangibly included in a machinereadable medium, such as the storage unit 908. In some embodiments, partor all of the computer program may be loaded and/or installed on thedevice 900 via the ROM 902 and/or the communication unit 909. When thecomputer program is loaded into the RAM 903 and executed by thecomputation unit 901, one or more steps of the method for processingbehavior data or the method for controlling an autonomous vehicledescribed above may be performed. Alternatively, in other embodiments,the computation unit 901 may be configured to perform the method forprocessing behavior data or the method for controlling an autonomousvehicle by any other appropriate means (for example, by means offirmware).

The autonomous vehicle provided by the present disclosure may includethe above electronic device as shown in FIG. 9 , and the electronicdevice may implement the method for controlling an autonomous vehicledescribed in the above embodiment, when executed by a processor.

Various implementations of the systems and technologies described aboveherein may be implemented in a digital electronic circuit system, anintegrated circuit system, a field programmable gate array (FPGA), anapplication specific integrated circuit (ASIC), an application specificstandard product (ASSP), a system on chip (SOC), a complex programmablelogic device (CPLD), computer hardware, firmware, software, and/or acombination thereof. The various implementations may include: animplementation in one or more computer programs that are executableand/or interpretable on a programmable system including at least oneprogrammable processor, which may be a special-purpose orgeneral-purpose programmable processor, and may receive data andinstructions from, and transmit data and instructions to, a storagesystem, at least one input apparatus, and at least one output apparatus.

Program codes for implementing the method of the present disclosure maybe compiled using any combination of one or more programming languages.The program codes may be provided to a processor or controller of ageneral-purpose computer, a special-purpose computer, or otherprogrammable data processing apparatuses, such that the program codes,when executed by the processor or controller, cause thefunctions/operations specified in the flow charts and/or block diagramsto be implemented. The program codes may be completely executed on amachine, partially executed on a machine, executed as a separatesoftware package on a machine and partially executed on a remotemachine, or completely executed on a remote machine or server.

In the context of the present disclosure, the machine-readable mediummay be a tangible medium which may contain or store a program for useby, or used in combination with, an instruction execution system,apparatus or device. The machine-readable medium may be amachine-readable signal medium or a machine-readable storage medium. Themachine-readable medium may include, but is not limited to, electronic,magnetic, optical, electromagnetic, infrared, or semiconductor systems,apparatuses, or devices, or any appropriate combination of the above. Amore specific example of the machine-readable storage medium willinclude an electrical connection based on one or more pieces of wire, aportable computer disk, a hard disk, a random-access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor flash memory), an optical fiber, a portable compact disk read-onlymemory (CD-ROM), an optical storage device, a magnetic storage device,or any appropriate combination of the above.

To provide interaction with a user, the systems and technologiesdescribed herein may be implemented on a computer that is provided with:a display apparatus (e.g., a CRT (cathode ray tube) or a LCD (liquidcrystal display) monitor) configured to display information to the user;and a keyboard and a pointing apparatus (e.g., a mouse or a trackball)by which the user can provide an input to the computer. Other kinds ofapparatuses may be further configured to provide interaction with theuser. For example, a feedback provided to the user may be any form ofsensory feedback (e.g., visual feedback, auditory feedback, or hapticfeedback); and an input may be received from the user in any form(including an acoustic input, a voice input, or a tactile input).

The systems and technologies described herein may be implemented in acomputing system (e.g., as a data server) that includes a back-endcomponent, or a computing system (e.g., an application server) thatincludes a middleware component, or a computing system (e.g., a usercomputer with a graphical user interface or a web browser through whichthe user can interact with an implementation of the systems andtechnologies described herein) that includes a front-end component, or acomputing system that includes any combination of such a back-endcomponent, such a middleware component, or such a front-end component.The components of the system may be interconnected by digital datacommunication (e.g., a communication network) in any form or medium.Examples of the communication network include: a local area network(LAN), a wide area network (WAN), and the Internet.

The computer system may include a client and a server. The client andthe server are generally remote from each other, and usually interactvia a communication network. The relationship between the client and theserver arises by virtue of computer programs that run on correspondingcomputers and have a client-server relationship with each other. Theserver may be a cloud server, is also known as a cloud computing serveror a cloud host, and is a host product in a cloud computing servicesystem to solve the defects of difficult management and weak serviceextendibility existing in conventional physical hosts and virtualprivate servers (VPS); or may be a distributed system server, or may bea server combined with a blockchain.

It should be understood that the various forms of processes shown abovemay be used to reorder, add, or delete steps. For example, the stepsdisclosed in the present disclosure may be executed in parallel,sequentially, or in different orders, as long as the desired results ofthe technical solutions provided in the present disclosure can beimplemented. This is not limited herein.

In the technical solution of the present disclosure, the acquisition,storage, and application of the user personal information involved areall in compliance with the relevant laws and regulations, and do notviolate public order and good customs.

The above specific embodiments do not constitute a limitation on theprotection scope of the present disclosure. It should be understood bythose skilled in the art that various modifications, combinations,subcombinations and substitutions may occur depending on designrequirements and other factors. Any modifications, equivalentreplacements, and improvements made within the spirit and principle ofthe present disclosure should be included within the protection scope ofthe present disclosure.

What is claimed is:
 1. A method for processing behavior data, the methodcomprising: acquiring historical driving data, the historical drivingdata comprising lane-level navigation data; and performing data miningon the historical driving data to obtain driving feature information,the driving feature information comprising at least one of: alane-change position feature, a traveling speed feature, or a travelingpath feature.
 2. The method according to claim 1, wherein the historicaldriving data further comprises in-vehicle image data; and the drivingfeature information comprises at least one of: a dangerous scenariofeature, a dynamic event feature, and a road-surface condition feature.3. The method according to claim 1, wherein the driving featureinformation comprises the lane-change position feature, and theperforming data mining on the historical driving data to obtain drivingfeature information, comprises: acquiring lane-change data in aplurality of lane-change scenarios from the lane-level navigation data,wherein the lane-change scenarios comprise an intersection lane-changescenario; performing classifying, for lane-change data in eachlane-change scenario, to obtain a plurality of sets of lane-change dataaccording to a traffic flow level; and performing lane-change positionclustering on each set of lane-change data respectively to obtain aplurality of lane-change position features, wherein each lane-changeposition feature corresponds to a traffic flow level in a lane-changescenario.
 4. The method according to claim 1, wherein the drivingfeature information comprises the traveling speed feature, and theperforming data mining on the historical driving data to obtain drivingfeature information, comprises: acquiring traveling speed data atdifferent geographic positions from the lane-level navigation data; andclustering traveling speed data at each geographic position respectivelyto obtain an average speed value, an average acceleration value and anextremum speed value at the geographic position, as the traveling speedfeature.
 5. The method according to claim 1, wherein the driving featureinformation comprises the traveling path feature, and the performingdata mining on the historical driving data to obtain driving featureinformation, comprises: acquiring traveling path data from thelane-level navigation data; and classifying the traveling path data toobtain a recommended path feature and a dangerous path feature as thetraveling path feature.
 6. The method according to claim 2, wherein thedriving feature information comprises the dangerous scenario feature,and the performing data mining on the historical driving data to obtaindriving feature information, comprises: extracting driving scenario datafrom the in-vehicle image data; classifying the driving scenario data toobtain a plurality of dangerous scenarios, wherein the plurality ofdangerous scenarios comprise a curve-road scenario, a laneway scenario;and using image features in each dangerous scenario as the dangerousscenario feature.
 7. The method according to claim 2, wherein thedriving feature information comprises the dynamic event feature, and theperforming data mining on the historical driving data to obtain drivingfeature information, comprises: identifying a dynamic event in thein-vehicle image data, wherein the dynamic event comprises at least oneof: a construction event, or an accident event; and using anidentification result as the dynamic event feature.
 8. The methodaccording to claim 2, wherein the driving feature information comprisesthe road-surface condition feature, and the performing data mining onthe historical driving data to obtain driving feature information,comprises: extracting road-surface image data from the in-vehicle imagedata; and identifying the road-surface image data to obtain theroad-surface condition feature, wherein the road-surface conditionfeature comprises at least one of: a wear feature of a road surface, aslippery feature of a road surface, a pothole feature of a road surface,or an obstacle feature of a road surface.
 9. The method according toclaim 1, wherein the method further comprises: acquiring perceptualpositioning information at a vehicle end; generating drivingdecision-making information based on the driving feature information andthe perceptual positioning information; and controlling the vehicle,based on the driving decision-making information.
 10. The methodaccording to claim 9, wherein the method further comprises: acquiringV2X information for vehicle wireless communication; and the generatingdriving decision-making information based on the driving featureinformation and the perceptual positioning information, comprises:generating the driving decision-making information based on the drivingfeature information, the perceptual positioning information and the V2Xinformation.
 11. An apparatus for processing behavior data, theapparatus comprising: at least one processor; and a memory storinginstructions, wherein the instructions when executed by the at least oneprocessor, cause the at least one processor to perform operations, theoperations comprising: acquiring historical driving data, the historicaldriving data comprising lane-level navigation data; and performing datamining on the historical driving data to obtain driving featureinformation, the driving feature information comprising at least one of:a lane-change position feature, a traveling speed feature, or atraveling path feature.
 12. The apparatus according to claim 11, whereinthe historical driving data further comprises in-vehicle image data; andthe driving feature information comprises at least one of: a dangerousscenario feature, a dynamic event feature, and a road-surface conditionfeature.
 13. The apparatus according to claim 11, wherein the drivingfeature information comprises the lane-change position feature, and theperforming data mining on the historical driving data to obtain drivingfeature information comprises: acquiring lane-change data in a pluralityof lane-change scenarios from the lane-level navigation data, whereinthe lane-change scenarios comprise an intersection lane-change scenario;performing classifying, for lane-change data in each lane-changescenario, to obtain a plurality of sets of lane-change data according toa traffic flow level; and performing lane-change position clustering oneach set of lane-change data respectively to obtain a plurality oflane-change position features, wherein each lane-change position featurecorresponds to a traffic flow level in a lane-change scenario.
 14. Theapparatus according to claim 11, wherein the driving feature informationcomprises the traveling speed feature, and the performing data mining onthe historical driving data to obtain driving feature informationcomprises: acquiring traveling speed data at different geographicpositions from the lane-level navigation data; and clustering travelingspeed data at each geographic position respectively to obtain an averagespeed value, an average acceleration value and an extremum speed valueat the geographic position, as the traveling speed feature.
 15. Theapparatus according to claim 11, wherein the driving feature informationcomprises the traveling path feature, and the performing data mining onthe historical driving data to obtain driving feature informationcomprises: acquiring traveling path data from the lane-level navigationdata; and classifying the traveling path data to obtain a recommendedpath feature and a dangerous path feature as the traveling path feature.16. The apparatus according to claim 12, wherein the driving featureinformation comprises the dangerous scenario feature, and the performingdata mining on the historical driving data to obtain driving featureinformation comprises: extracting driving scenario data from thein-vehicle image data; classifying the driving scenario data to obtain aplurality of dangerous scenarios, wherein the plurality of dangerousscenarios comprise a curve-road scenario, a laneway scenario; and usingimage features in each dangerous scenario as the dangerous scenariofeature.
 17. The apparatus according to claim 12, wherein the drivingfeature information comprises the dynamic event feature, and theperforming data mining on the historical driving data to obtain drivingfeature information comprises: identifying a dynamic event in thein-vehicle image data, wherein the dynamic event comprises at least oneof: a construction event, or an accident event; and using anidentification result as the dynamic event feature.
 18. The apparatusaccording to claim 12, wherein the driving feature information comprisesthe road-surface condition feature, and the performing data mining onthe historical driving data to obtain driving feature informationcomprises: extracting road-surface image data from the in-vehicle imagedata; and identifying the road-surface image data to obtain theroad-surface condition feature, wherein the road-surface conditionfeature comprises at least one of: a wear feature of a road surface, aslippery feature of a road surface, a pothole feature of a road surface,or an obstacle feature of a road surface.
 19. The apparatus according toclaim 11, wherein the operations further comprise: acquiring perceptualpositioning information at a vehicle end; generating drivingdecision-making information based on the driving feature information andthe perceptual positioning information; and controlling the vehicle,based on the driving decision-making information.
 20. A non-transitorycomputer readable storage medium storing computer instructions, wherein,the computer instructions are used to cause the computer to performoperations comprising: acquiring historical driving data, the historicaldriving data comprising lane-level navigation data; and performing datamining on the historical driving data to obtain driving featureinformation, the driving feature information comprising at least one of:a lane-change position feature, a traveling speed feature, or atraveling path feature.